EmptyDroplets (FDR <= 0.1) + scDblFindersetwd("/media/jacopo/Elements/re_align/MM/PRJNA723584/SAMN18822743/SRR14295358/")
# Load the libraries (from Sarah script + biomart)
library(tidyverse) # packages for data wrangling, visualization etc
library(Seurat) # scRNA-Seq analysis package
library(clustree) # plot of clustering tree
library(ggsignif) # Enrich your 'ggplots' with group-wise comparisons
library(clusterProfiler) #The package implements methods to analyze and visualize functional profiles of gene and gene clusters.
library(org.Hs.eg.db) # Human annotation package neede for clusterProfiler
library(ggrepel) # extra geoms for ggplo2
library(patchwork) #multiplots
library(reticulate)
Load and do the QC for the cellranger data
#list.files(".")
dat <- Read10X(data.dir ="./out/counts_filtered/")
dat <- CreateSeuratObject(dat) # Create the seurat object from the 10x data
kb.initial <- dat@assays[["RNA"]]@counts@Dim[[2]]
cat("Initial number of cells:", kb.initial,
"\nNumber of genes:", dat@assays[["RNA"]]@counts@Dim[[1]])
## Initial number of cells: 19764
## Number of genes: 36601
Empty cells were already filtered, check for % mt RNA and death markers:
# first calculate the mitochondrial percentage for each cell
dat$percent_mt <- PercentageFeatureSet(dat, pattern="^MT.")
# make violin plots
mt_rna = 15
max_counts = 35000
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
VlnPlot(dat, features = c("nCount_RNA", "nFeature_RNA", "percent_mt")) + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot1 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "percent_mt")
plot1 <- plot1 + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot2 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2 <- plot2 + geom_vline(xintercept = max_counts, linetype = "dotted")
plot1
plot2
## cells retained by mt RNA content ( 15 %): 19045
## percentage of retained cells: 96.36 %
## cells retained by counts ( 35000 ): 18988
## percentage of retained cells: 96.07 %
Check the distribution of the cells with low counts and control death markers:
min_counts = 800
hist(dat@meta.data$nCount_RNA, breaks = 100, xlab = "Counts")
hist(dat@meta.data$nCount_RNA, breaks = 1000, xlab = "Counts", xlim = c(0,5000))
hist(dat@meta.data$nCount_RNA, breaks = 10000, xlab = "Counts", xlim = c(0,1000))
abline(v=min_counts, col="red", lty = 3)
The evident peak of cells with < 200 counts could contain dying
cells.
# Subset the dataset to focus only on those cells with low counts
dat.lowcount <- subset(dat, subset = nCount_RNA < min_counts)
# Get the mean of the counts for each gene and sort them decreasing
meanCounts <- rowMeans(GetAssayData(object = dat.lowcount, slot = 'counts'))
meanCounts <- sort(meanCounts, decreasing = T)
# A boxplot can help to observe the distribution of the means
#boxplot(meanCounts)
# Print the most highly expressed genes
head(meanCounts, 30)
## IGLC2 RPL10 IGHG1 RPLP1 EEF1A1 B2M IGHG3
## 28.8044128 3.1740386 2.6512012 2.2430147 1.6290339 1.3803485 1.2767296
## RPS14 RPL41 RPL3 RPS12 RPS19 RPS18 RPL7A
## 1.2240437 1.1761006 1.1506341 1.1338282 1.1010413 1.0979482 1.0395917
## IGHGP RPL18A RPL32 RPL13 RPL28 RPL29 RPS4X
## 1.0233014 0.9504073 0.9396845 0.9334983 0.8669966 0.8307042 0.7854418
## RPL19 RPS27A RPL12 RPS3A RPL18 RPS8 RPS6
## 0.7540984 0.7534797 0.7410042 0.7372925 0.7335808 0.7098670 0.6898649
## RPS23 RPL17
## 0.6867718 0.6588308
## cells retained by counts ( 800 ): 9289
## percentage of retained cells: 47 %
dir.create("result")
saveRDS(dat, file = "./result/SAMN18822743_clean_QC.Rds")
#Normalize
dat <- NormalizeData(dat)
# Find the first 4000 variabe features
dat <- FindVariableFeatures(dat, selection.method = "vst", nfeatures = 4000)
Set mean expression to 0 and variance across 1 to avoid highly expressed genes drive the forwarding analyses. Since negative expression is meaningless, scaled data are useful only for UMAP and clustering
# scale data, the scaled data are saved in:
# dat[["RNA"]]@scale.data
all.genes <- rownames(dat)
dat <- ScaleData(dat, vars.to.regress = c("percent_mt","nCount_RNA"))
dat <- RunPCA(dat, features = VariableFeatures(object = dat), verbose = F, seed.use = 1)
print(dat[["pca"]], dims = 1:5, nfeatures = 5)
## PC_ 1
## Positive: HBA1, HBA2, HBB, ALAS2, IGLC3
## Negative: RPL10, RPLP1, RPS14, RPL7A, RPL32
## PC_ 2
## Positive: B2M, FBXO32, RPL10, CYBA, IGLC3
## Negative: MKI67, NUSAP1, PCLAF, BIRC5, TYMS
## PC_ 3
## Positive: RPS4X, RPS12, RPS2, RPS6, RPL7A
## Negative: MALAT1, NEAT1, TXNIP, JUND, AHNAK
## PC_ 4
## Positive: B2M, CYBA, ITGB7, MZB1, STMN1
## Negative: HBB, HBA1, HBA2, HBD, HBM
## PC_ 5
## Positive: MZB1, B2M, PRDX4, HLA-DRA, CD81
## Negative: MALAT1, NEAT1, KLF2, TXNIP, JUND
UMAP is a graph-based method of clustering. The first step in this process is to construct a KNN graph based on the euclidean distance in PCA space:
dat <- FindNeighbors(dat, dims = 1:20)
The graph now can be used as input for the function
runUMAP()
dat <- RunUMAP(dat, dims = 1:20, seed.use = 1)
DimPlot(dat, reduction = 'umap', seed = 1)
## QC metrics
## markers